The study seeks to determine whether a powered, cable-driven exosuit has the potential to lower the lumbar muscle activity and overall metabolic expenditure of symmetric and asymmetric lifting tasks. A lightweight, cable-driven back exosuit, using a three-state impedance controller, was developed to provide variable assistance based on user posture. Experimental electromyography (EMG), metabolic cost, and user preference data were recorded for ten participants evaluated wearing the powered back exosuit versus the backX, a commercially available passive back support exoskeleton, and a no exo baseline. Both exoskeletons significantly reduced (p$< $0.05) muscle activation of certain lumbar flexor and extensor muscles when compared to a no exo condition across all conditions tested, though neither significantly reduced the metabolic cost associated with lifting. Users tended to prefer lifting with the powered device as opposed to the passive or no exo condition. Despite the increased mass of powered back support exoskeletons, these devices can reduce lumbar muscle activity to a similar degree as passive exoskeletons, and are favored by users over their passive counterparts. While current powered back support devices tend to incur the cost of being heavy, rigid, and inconvenient for certain lifting postures, these results show that cable-driven powered devices may minimize these factors to the point that they are favored over the currently popular passive devices on the market.

Effect of the temperature on the impedance control of pressure-based, current-driven electroacoustic absorbers: Addressing the loss of passivity using a viscoelastic material model

Stiffness optimization control in mobile manipulator drilling: an adaptive impedance control strategy for improving hole machining quality

During constant-force operations in complex marine environments, underwater manipulators are affected by hydrodynamic disturbances and unknown, time-varying environment stiffness. Under classical impedance control (IC), this often leads to large transient contact forces and steady-state force errors, making high-precision compliant control difficult to achieve. To address this issue, this study proposes a Bayesian recursive least-squares-based fuzzy adaptive impedance control (BRLS-FAIC) strategy with displacement correction for underwater manipulators. Within a position-based impedance-control framework, a Bayesian Recursive Least Squares (BRLS) stiffness identifier is constructed by incorporating process and measurement noise into a stochastic regression model, enabling online estimation of the environment stiffness and its covariance under noisy, time-varying conditions. The identified stiffness is used in a displacement-correction law derived from the contact model to update the reference position, thereby removing dependence on the unknown environment location and reducing steady-state force bias. On this basis, a three-input/two-output fuzzy adaptive impedance tuner, driven by the force error, its rate of change, and a stiffness-perception index, adjusts the desired damping and stiffness online under amplitude limitation and first-order filtering. Using an underwater manipulator dynamic model that includes buoyancy and hydrodynamic effects, MATLAB simulations are carried out for step, ramp, and sinusoidal stiffness variations and for planar, inclined, and curved contact scenarios. The results show that, compared with classical IC and fuzzy adaptive impedance control (FAIC), the proposed BRLS-FAIC strategy reduces steady-state force errors, shortens force and position settling times, and suppresses peak contact forces in variable-stiffness underwater environments.

In this paper, the One-Legged robot is designed to stabilize itself and stand upright at the desired location after being thrown from a different heights. The 5-DOF planar underactuated main body is driven by Reaction wheels, and adaptive Cartesian impedance control has been implemented to effectively manage hard impacts. Evolutionary Reinforcement Learning based AI Agent have been used to adapt to different launch conditions, such as varying speed and altitude. The learning process was performed in real-time using the Matlab simulation program, which models the system dynamics of the robot. The graphical results of the simulation confirm that, with the assistance of the AI agent, the dynamic robot has successfully maintained its stability without tipping over after the launch and has been able to make the desired correction.

The 4-PS parallel mechanism, with four kinematic chains, excels in heavy-load, large-stroke, and high-dynamic-response scenarios. However, when operating in complex dynamic environments with external disturbances and contact interactions, the 4-PS parallel mechanism exhibits significant degradation in pose tracking accuracy and contact force regulation. To address these challenges, this paper addresses two critical issues concerning the mechanism: contact dynamics and control under model uncertainties and external disturbances. First, a comprehensive dynamic model incorporating parametric model uncertainties and external disturbances is constructed for the 4-PS parallel mechanism, alongside its environmental contact model. Second, to compensate for model uncertainties and external disturbances, a fuzzy approximation system is employed to estimate sliding mode switching functions, augmented by an adaptive strategy to eliminate approximation residuals, thereby ensuring highly accurate pose tracking. Simultaneously, a fuzzy impedance controller is designed with force error and its rate as inputs to dynamically adjust damping and stiffness coefficients, achieving high-precision force tracking. Finally, simulations in a coal mine temporary support scenario demonstrate that the proposed adaptive fuzzy sliding mode impedance control strategy enables the mechanism to converge rapidly and accurately to the desired states, validating its effectiveness and reliability in simultaneous pose and force tracking for the 4-PS parallel mechanism.

Adaptive virtual impedance control strategy based on IWOA-fuzzy PID and its application to reactive power sharing in islanded microgrids.

This paper introduces an adaptive impedance control strategy for robotic manipulators, developed through the extremum seeking technique. A model-based disturbance observer (DOB) is employed to estimate contact forces, removing the dependency on torque sensors. An impedance vector is constructed to correct the errors arising from motor uncertainties and unknown couplings, without considering the threshold value of the control parameters. Joint tracking errors and fluctuations in contact force are incorporated into the cost function. For various tasks, suitable control parameters are adaptively optimized in real time using an extremum seeking approach, which continuously evaluates the cost function. A rigorous analysis is conducted on the stability of the proposed controller. Compared to conventional approaches, the proposed adaptive impedance control offers a more streamlined design for adjusting the manipulator’s contact impedance. Experimental results confirm that the extremum seeking strategy successfully tuned the controller parameters online according to variations in the cost function.

Silicon carbide (SiC) three-phase converters are widely adopted in parallel power distribution systems for their high efficiency, yet their performance is challenged by high switching frequency and communication constraints. For the parallel inverter system, problems such as uneven power distribution and circulating current may occur. Therefore, the droop control method was proposed. The droop control method is limited in precise power sharing and circulating current mitigation. To address these issues in the communication-free parallel inverter system, a hybrid droop-enhanced virtual impedance method is proposed. The methodology integrates droop characteristics with frequency-selective virtual impedance compensation, enabling concurrent optimization of power sharing and circulating current suppression. Through simulation, the droop control method and the improved droop control method were compared and analyzed. Finally, the effectiveness of the improved droop control method was verified through experiments.

This paper proposes a safety-constrained interaction control scheme for robotic manipulators by integrating model predictive control (MPC) and active disturbance rejection control (ADRC). The proposed method is specifically designed for manipulators with complex nonlinear dynamics. To ensure that the control system satisfies safety constraints during human–robot interaction, MPC is incorporated into the impedance control framework to construct a model predictive impedance controller (MPIC). By exploiting the prediction and constraint-handling capabilities of MPC, the controller provides guaranteed safety throughout the interaction process. Meanwhile, ADRC is employed to track the target joint control signals generated by the MPIC, where an extended state observer is utilized to compensate for dynamic modeling errors and nonlinear disturbances within the system, thereby achieving accurate trajectory tracking. The proposed method is validated through both simulation and real-world experiments, achieving high-performance interaction control with safety constraints at a 2 ms control cycle. The controller exhibits active compliant interaction behavior when the interaction stays within the constraint boundaries, while maintaining strict adherence to the safety constraints when the interaction tends to violate them.

The increasing demand for compact, high-performance antennas capable of supporting multiple wireless communication standards has driven the development of multiband microstrip antennas. This research presents the design, simulation, and optimization of a multiband microstrip patch antenna operating at 2.4 GHz, 3.5 GHz, and 5.3 GHz, targeting applications in Wi-Fi and WLAN systems. The antenna structure is designed and analyzed using CST Microwave Studio, leveraging its full-wave 3D electromagnetic solver to evaluate key performance metrics including reflection coefficient (S11), gain, bandwidth, and radiation characteristics. To enhance the antenna's performance and reduce the design iteration cycle, support vector regression (SVR), a supervised machine learning technique, is employed. SVR models the nonlinear relationship between the antenna's geometric parameters and its performance outcomes, enabling efficient prediction and optimization. A dataset of 1844 samples is generated through parametric simulations in CST, and the SVR model—using a radial basis function kernel with C = 300, ε = 0.00000000025, and γ = 0.5—is trained to predict return loss and gain across the three target frequencies. The optimized antenna design achieves improved impedance matching, gain enhancement, and bandwidth control at all three frequency bands. Power transfer efficiency exceeds 96% in each band. The results demonstrate that the integration of SVR into the antenna design workflow provides a robust, data-driven approach to achieving multiband performance with high efficiency, making it suitable for next-generation wireless communication systems. Received: 11 August 2025 | Revised: 15 October 2025 | Accepted: 27 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Nejat Abdulwahid Hassen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Visualization, Supervision. Adomeas Asfaw Tafere: Conceptualization, Methodology, Software, Validation, Resources, Data curation,Writing - review &amp; editing, Visualization,Supervision. Murad Ridwan Hassen: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Writing - original draft, Visualization, Supervision, Project administration. Tsega Asresa Mengistu: Conceptualization, Software, Validation, Resources, Writing - review &amp; editing, Visualization, Supervision. Mekete Asmare Huluka: Conceptualization, Software, Validation, Resources, Writing - review &amp; editing, Visualization, Supervision. Amsalu Tessema Adgeh: Conceptualization, Software, Formal analysis, Resources, Writing - review &amp; editing, Visualization.

Manipulating complex objects is ubiquitous in our daily activities, such as donning a jacket or carrying a cup of coffee. However, such non-rigid objects easily become unstable: When carrying a cup of coffee, the coffee could slosh unpredictably and spill out of the cup. It remains unclear what motor control strategies ensure stability, especially when the physical properties of the object, like the amount of liquid in the cup, are unknown. The task of transporting a 'cup of coffee' was simplified to transporting a virtual cup with a sliding ball inside, modeled as a cart-pendulum system. Participants were instructed to 'jiggle' the cup in one dimension to prepare the cup and ball states for the ensuing continuous rhythmic movement. To introduce uncertainty regarding the object's properties, the pendulum's length was manipulated either to 1) change randomly from trial to trial, or to 2) remain constant across trials. We measured the ball's angle at the end of preparation and the cup's oscillation frequency during the rhythmic portion of the trial. Grip force on the robot handle served as proxy for mechanical impedance of the arm. The results supported three predictions: 1) When dynamic uncertainty was high, object preparation was important to stabilize the transient dynamics; stability increased during preparation and humans prepared longer when the dynamics was uncertain. 2) Humans maximized dynamic stability by flexibly covarying system initialization and cup frequency; dynamic stability matched participant behavior better than magnitude or smoothness of force. 3) Humans increased their arm impedance to accommodate uncertain dynamics, while the net force applied on the cup remained the same. Feedforward simulations using an impedance controller and stochastic open-loop optimal control corroborated these findings, further revealing that participants' selection of preparation and interaction frequencies also minimized mechanical impedance. In sum, humans used preparation and interaction strategies to optimize the mechanical impedance and dynamic stability of the hand-object interactions. These results may inform approaches in robotic control and rehabilitation.

Manipulating complex objects is ubiquitous in our daily activities, such as donning a jacket or carrying a cup of coffee. However, such non-rigid objects easily become unstable: When carrying a cup of coffee, the coffee could slosh unpredictably and spill out of the cup. It remains unclear what motor control strategies ensure stability, especially when the physical properties of the object, like the amount of liquid in the cup, are unknown. The task of transporting a ‘cup of coffee’ was simplified to transporting a virtual cup with a sliding ball inside, modeled as a cart-pendulum system. Participants were instructed to ‘jiggle’ the cup in one dimension to prepare the cup and ball states for the ensuing continuous rhythmic movement. To introduce uncertainty regarding the object’s properties, the pendulum’s length was manipulated either to 1) change randomly from trial to trial, or to 2) remain constant across trials. We measured the ball’s angle at the end of preparation and the cup’s oscillation frequency during the rhythmic portion of the trial. Grip force on the robot handle served as proxy for mechanical impedance of the arm. The results supported three predictions: 1) When dynamic uncertainty was high, object preparation was important to stabilize the transient dynamics; stability increased during preparation and humans prepared longer when the dynamics was uncertain. 2) Humans maximized dynamic stability by flexibly covarying system initialization and cup frequency; dynamic stability matched participant behavior better than magnitude or smoothness of force. 3) Humans increased their arm impedance to accommodate uncertain dynamics, while the net force applied on the cup remained the same. Feedforward simulations using an impedance controller and stochastic open-loop optimal control corroborated these findings, further revealing that participants’ selection of preparation and interaction frequencies also minimized mechanical impedance. In sum, humans used preparation and interaction strategies to optimize the mechanical impedance and dynamic stability of the hand-object interactions. These results may inform approaches in robotic control and rehabilitation.

The number of stroke patients is steadily increasing, highlighting the critical need for rehabilitation to restore motor function. The shortage of rehabilitation specialists and the repetitive nature of traditional exercises underscore the advantages of robotic systems. However, many existing rehabilitation robots are not adaptable to various therapeutic requirements. Modular robots, with their reconfigurability and ease of assembly, provide a practical solution. This study presents the design and development of a modular wrist rehabilitation robot, consisting of three identical modules that provide the necessary degrees of freedom for wrist movement. The modular structure allows for easy assembly, adjustment for different hand sizes, and adaptability to various configurations. To improve control performance, an impedance control strategy was implemented along with a gravity compensation method. Simulation results show that impedance control alone resulted in an RMS error of approximately 7 degrees, while adding gravity compensation reduced the maximum error to 0.79 degrees. The proposed controller was implemented on the robot and validated through experimental tests with individuals. The results confirmed that impedance control effectively facilitates interaction between the robot and the user, demonstrating the system’s potential for improving rehabilitation outcomes.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-31185-w.

This paper presents a methodology to estimate the stiffness and damping of a novel variable impedance actuator designed to adjust its effective impedance by regulating the temperature of a thermoresponsive polymer, Polycaprolactone (PCL). The actuator’s PCL temperature is controlled using embedded flexible Peltier elements. However, due to the absence of internal temperature sensors, it is necessary to develop a reliable estimation method for effective stiffness and damping during harmonic motion. The proposed approach leverages experimental data, including trajectory measurements and phase delay analyses, to estimate these parameters under varying temperatures and frequencies. Experimental results demonstrate that stiffness and damping can be effectively modulated by altering the PCL temperature, with higher temperatures leading to decreased stiffness and damping due to reduced viscoelasticity. Additionally, it was observed that the system’s dynamic response is frequency-dependent, which presents a challenge for precise impedance regulation solely through temperature control. Despite this limitation, the proposed estimation methodology accurately captures the system’s viscoelastic behavior, offering valuable insights into the actuator’s performance and potential for applications requiring adaptive impedance control.

Current upper-limb rehabilitation robots primarily focus on training tasks involving free movements or static interactions with rigid objects. These paradigms lack simulation of complex object manipulation tasks encountered in daily life, thereby limiting the training of patients’ high-level sensorimotor integration capabilities. To address this gap, this study proposes an innovative robotic rehabilitation training system designed for functional occupational therapy. Specifically, the task of transporting a water cup was abstracted into a cup–ball system integrated with a robotic arm. The ball was modeled as a point mass, and kinematic and dynamic analyses of the system were conducted. A visual tracking method was employed to monitor the ball’s motion and calculate its slosh angle. Owing to the impaired fine motor control in stroke patients, a sloshing suppression control strategy integrating exponential filtering, feedforward force compensation, and impedance control was proposed to prevent the ball from spilling. Experiments validated the effectiveness of the proposed method. The results indicated that with full compensation, the oscillation rate of the ball was significantly reduced, and the smoothness of the hand force was markedly improved. This study presents an effective method for addressing dynamic uncertainty in rehabilitation robot training, thus significantly improving the functional relevance of the training.

Game theory based vision impedance control for human-robot interaction.

Elbow orthoses offer a straightforward mechanical approach to rehabilitation following elbow joint injuries. The convergence of artificial intelligence with these orthoses has given rise to elbow rehabilitation robots, designed to address the personalized rehabilita-tion requirements of patients with diverse injury profiles. A typical elbow rehabilitation robot system comprises four key components: a mechanical rigid structure, an actuation system, bionic sensors, and integrated software. The rigid structure, analogous to the human skeletal system, includes linkage assemblies and gear transmission systems. The actuation system, mimicking muscles and ligaments, generates and modulates the necessary forces and torques for movement, employing actuators such as pneumatic, elastic (e.g., series elastic actuators), and cable-driven types. Bionic sensors, serving as the robot's perceptual interface, encompass photoelectric encoders, force/torque sensors, electromyo-graphic (EMG) signal sensors, and temperature sensors. The software system-encompassing control algorithms and machine learning models-functions as the "neural center," analogous to the brain and spinal cord, and is responsible for intelligent decision-making and motion control. The core technological achievement lies in seamlessly integrating hardware and software to enable precise tracking of elbow joint kinematics and adaptive modulation of assistive forces based on real-time human-robot interaction. This integration supports multiple training modalities-including passive, assistive, active, and resistive modes-to deliver safe, tailored, and intelligent rehabilitation support across different recovery phases. By harnessing technologies like bio-inspired design, precise impedance control, EMG-based assistance, and virtual reality integrated task training, these robotic systems improve training comfort, assistance accuracy, and patient adherence, potentially reducing post-injury disability rates. This review outlines the current state of research in elbow rehabilitation robotics, details the key system components and primary training modalities, discusses clinical demands and future development trends, and aims to offer insights for the further refinement of rehabilitation robotic systems.

Handedness and brain lateralization: A nonlinear motor approach combined with EEG.

Decentralized adaptive virtual impedance control for robust power sharing and circulating current suppression in parallel inverters for three-phase islanded microgrid applications